The rapid growth of image acquisition and storage technologies have resulted in a wealth of data. Algorithms based on raw image data such as deep convolutional neural networks (CNNs) [1]} leverage such large data sets to obtain high accuracy for tasks such as classification and prediction, and have subsequently been extensively used for such tasks in medical contexts. A central challenge remains efficiently processing large amounts of 3D medical image data, particularly in the case of computational algorithms such as deep neural networks which require large amounts of training data, memory and computational power and are limited by algorithmic complexity, bandwidth and RAM capacity. This motivates the use of sparse, data-efficient image representations such as SIFT keypoints [2]}, which enable machine learning algorithms to manipulate sets of training data that are several orders of magnitude larger than if the raw image data were to be used. This is especially true for analyzing volumetric medical image data, where at the finest grain of analysis each individual subject may represent a unique class. For example, the ability to identify and characterize individuals and family members in large sets 3D scans is crucial in achieving accurate, personalized healthcare and minimizing patient-level labeling errors. Currently the 3D SIFT-Rank keypoint indexing [3]} is the only method reporting this capability [4]}, [5]}.
